A supply chain is a network of actions followed or practiced to achieve a common goal. One objective of the supply chain may typically be customer satisfaction. If the supply chain is organized properly or working properly without any disruption, then the supply chain may be considered value added. The net profitability from the supply chain may be higher than otherwise. On the other hand, if the supply chain is not behaving properly or meeting the objective, then the entire supply chain may result in losses.
Losses in the supply chain in a market may be due to the risk or disruption of the supply chain. Hence, organizations adopt their own methodologies for managing supply chain risks. Supply chain risk management may be practiced by several companies, which may strive to have the most optimized supply chain because doing so usually translates to lower costs for the company. Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors).
The inventors here have recognized several technical problems with such conventional systems, as explained below. As a proactive measure, companies would like to, but currently do not, have the ability to predict supply chain risks before they can cause actual damage to the entire supply chain. The prediction of risks may be tricky, however. Prediction models have to be build based on the history of the risks and how the system behaves, to properly mitigate these risks.
In order to build prediction models, the researchers may use simulation, data mining techniques, statistics, and machine learning techniques. While these risk prediction models may help in pointing out the occurrence of the risk as a probability estimate, currently, such prediction models are incapable of analyzing risks and taking appropriate action before the occurrence of the risk.
Previously, Failure Mode Effect Analysis (FMEA) has been used to analyze the risks and mitigate the supply chain risks. FMEA may be used to predict the risk priority number (RPN) in order to prioritize the high priority risks. The current risk prediction solutions available, however, are not able to:
1. Predict the future risks as a preventive measure
2. Understand the complexity of risks in the supply chain
3. Identify relationship of the various risks within the supply chain
4. Predict the future risks without any disruption to the supply chain